From 7f2a130d7bb1644bd1d1341c8cc8a7ef8e3a1b38 Mon Sep 17 00:00:00 2001
From: Florian Pfaff <6773539+FlorianPfaff@users.noreply.github.com>
Date: Tue, 4 Aug 2026 19:32:44 +0800
Subject: [PATCH 1/2] Validate Gaussian mixture parameter shapes

---
 .../nonperiodic/gaussian_mixture.py           | 24 ++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/src/pyrecest/distributions/nonperiodic/gaussian_mixture.py b/src/pyrecest/distributions/nonperiodic/gaussian_mixture.py
index f392b1a14e..8a1243835b 100644
--- a/src/pyrecest/distributions/nonperiodic/gaussian_mixture.py
+++ b/src/pyrecest/distributions/nonperiodic/gaussian_mixture.py
@@ -66,8 +66,30 @@ def covariance(self):
     def mixture_parameters_to_gaussian_parameters(
         means, covariance_matrices, weights=None
     ):
+        means = array(means)
+        covariance_matrices = array(covariance_matrices)
+
+        if means.ndim == 1:
+            n_components = means.shape[0]
+            state_dim = 1
+        elif means.ndim == 2:
+            n_components, state_dim = means.shape
+        else:
+            raise ValueError(
+                "means must have shape (n_components,) or (n_components, state_dim)"
+            )
+        if n_components == 0 or state_dim == 0:
+            raise ValueError("means must contain at least one nonempty component mean")
+
+        expected_covariance_shape = (state_dim, state_dim, n_components)
+        if covariance_matrices.shape != expected_covariance_shape:
+            raise ValueError(
+                "covariance_matrices must have shape "
+                f"{expected_covariance_shape}, got {covariance_matrices.shape}"
+            )
+
         if weights is None:
-            weights = ones(means.shape[0]) / means.shape[0]
+            weights = ones(n_components) / n_components
         else:
             weights = array(weights)
             if weights.ndim == 0:

From bc8d5788ac222a1c0b72187c01d43a13bb3e2c7b Mon Sep 17 00:00:00 2001
From: Florian Pfaff <6773539+FlorianPfaff@users.noreply.github.com>
Date: Tue, 4 Aug 2026 19:32:58 +0800
Subject: [PATCH 2/2] Test Gaussian mixture parameter shape validation

---
 .../test_gaussian_mixture_parameter_shapes.py | 54 +++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 tests/distributions/test_gaussian_mixture_parameter_shapes.py

diff --git a/tests/distributions/test_gaussian_mixture_parameter_shapes.py b/tests/distributions/test_gaussian_mixture_parameter_shapes.py
new file mode 100644
index 0000000000..b722f8ab57
--- /dev/null
+++ b/tests/distributions/test_gaussian_mixture_parameter_shapes.py
@@ -0,0 +1,54 @@
+import unittest
+
+import numpy as np
+import numpy.testing as npt
+
+# pylint: disable=no-name-in-module
+from pyrecest.backend import array, to_numpy
+from pyrecest.distributions.nonperiodic.gaussian_mixture import GaussianMixture
+
+
+class GaussianMixtureParameterShapeTest(unittest.TestCase):
+    def test_rejects_missing_component_covariance(self):
+        means = array([0.0, 2.0])
+        covariance_matrices = array([[[1.0]]])
+
+        with self.assertRaisesRegex(
+            ValueError,
+            "covariance_matrices must have shape",
+        ):
+            GaussianMixture.mixture_parameters_to_gaussian_parameters(
+                means,
+                covariance_matrices,
+                array([0.25, 0.75]),
+            )
+
+    def test_rejects_covariance_without_component_axis(self):
+        means = array([[0.0, 0.0], [1.0, 1.0]])
+        covariance_matrices = array([[1.0, 0.0], [0.0, 1.0]])
+
+        with self.assertRaisesRegex(
+            ValueError,
+            "covariance_matrices must have shape",
+        ):
+            GaussianMixture.mixture_parameters_to_gaussian_parameters(
+                means,
+                covariance_matrices,
+                array([0.5, 0.5]),
+            )
+
+    def test_matching_component_shapes_preserve_moment_matching(self):
+        mean, covariance = (
+            GaussianMixture.mixture_parameters_to_gaussian_parameters(
+                array([0.0, 2.0]),
+                array([[[1.0, 3.0]]]),
+                array([0.25, 0.75]),
+            )
+        )
+
+        npt.assert_allclose(to_numpy(mean), np.array([1.5]))
+        npt.assert_allclose(to_numpy(covariance), np.array([[3.25]]))
+
+
+if __name__ == "__main__":
+    unittest.main()